Browsing by Author "Sun, Qing"

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    Early Dynamic Orchestration of Immunologic Mediators Identifies Multiply Injured Patients who are Tolerant or Sensitive to Hemorrhage
    (Wolters Kluwer, 2021-03) McKinley, Todd O.; Gaski, Greg E.; Zamora, Ruben; Shen, Li; Sun, Qing; Namas, Rami A.; Billiar, Timothy R.; Vodovotz, Yoram; Orthopaedic Surgery, School of Medicine
    BACKGROUND Multiply injured patients (MIPs) are at risk of complications including infections, and acute and prolonged organ dysfunction. The immunologic response to injury has been shown to affect outcomes. Recent advances in computational capabilities have shown that early dynamic coordination of the immunologic response is associated with improved outcomes after trauma. We hypothesized that patients who were sensitive or tolerant of hemorrhage would demonstrate differences in dynamic immunologic orchestration within hours of injury. METHODS We identified two groups of MIPs who demonstrated distinct clinical tolerance to hemorrhage (n = 10) or distinct clinical sensitivity to hemorrhage (n = 9) from a consecutive cohort of 100 MIPs. Hemorrhage was quantified by integrating elevated shock index values for 24 hours after injury (shock volume). Clinical outcomes were quantified by average Marshall Organ Dysfunction Scores from days 2 to 5 after injury. Shock-sensitive patients had high cumulative organ dysfunction after lower magnitude hemorrhage. Shock-tolerant (ST) patients had low cumulative organ dysfunction after higher magnitude hemorrhage. Computational methods were used to analyze a panel of 20 immunologic mediators collected serially over the initial 72 hours after injury. RESULTS Dynamic network analysis demonstrated the ST patients had increased orchestration of cytokines that are reparative and protective including interleukins 9, 17E/25, 21, 22, 23, and 33 during the initial 0- to 8-hour and 8- to 24-hour intervals after injury. Shock-sensitive patients had delayed immunologic orchestration of a network of largely proinflammatory and anti-inflammatory mediators. Elastic net linear regression demonstrated that a group of five mediators could discriminate between shock-sensitive and ST patients. CONCLUSIONS Preliminary evidence from this study suggests that early immunologic orchestration discriminates between patients who are notably tolerant or sensitive to hemorrhage. Early orchestration of a group of reparative/protective mediators was amplified in shock-tolerant patients.
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    Mining and visualizing high-order directional drug interaction effects using the FAERS database
    (BMC, 2020) Yao, Xiaohui; Tsang, Tiffany; Sun, Qing; Quinney, Sara; Zhang, Pengyue; Ning, Xia; Li, Lang; Shen, Li; Obstetrics and Gynecology, School of Medicine
    Background: Adverse drug events (ADEs) often occur as a result of drug-drug interactions (DDIs). The use of data mining for detecting effects of drug combinations on ADE has attracted growing attention and interest, however, most studies focused on analyzing pairwise DDIs. Recent efforts have been made to explore the directional relationships among high-dimensional drug combinations and have shown effectiveness on prediction of ADE risk. However, the existing approaches become inefficient from both computational and illustrative perspectives when considering more than three drugs. Methods: We proposed an efficient approach to estimate the directional effects of high-order DDIs through frequent itemset mining, and further developed a novel visualization method to organize and present the high-order directional DDI effects involving more than three drugs in an interactive, concise and comprehensive manner. We demonstrated its performance by mining the directional DDIs associated with myopathy using a publicly available FAERS dataset. Results: Directional effects of DDIs involving up to seven drugs were reported. Our analysis confirmed previously reported myopathy associated DDIs including interactions between fusidic acid with simvastatin and atorvastatin. Furthermore, we uncovered a number of novel DDIs leading to increased risk for myopathy, such as the co-administration of zoledronate with different types of drugs including antibiotics (ciprofloxacin, levofloxacin) and analgesics (acetaminophen, fentanyl, gabapentin, oxycodone). Finally, we visualized directional DDI findings via the proposed tool, which allows one to interactively select any drug combination as the baseline and zoom in/out to obtain both detailed and overall picture of interested drugs. Conclusions: We developed a more efficient data mining strategy to identify high-order directional DDIs, and designed a scalable tool to visualize high-order DDI findings. The proposed method and tool have the potential to contribute to the drug interaction research and ultimately impact patient health care.